Overview

Dataset statistics

Number of variables14
Number of observations4416
Missing cells2714
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory483.1 KiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

num_orders is highly correlated with month and 8 other fieldsHigh correlation
month is highly correlated with num_orders and 3 other fieldsHigh correlation
lag_168 is highly correlated with num_orders and 7 other fieldsHigh correlation
lag_336 is highly correlated with num_orders and 6 other fieldsHigh correlation
lag_504 is highly correlated with num_orders and 6 other fieldsHigh correlation
lag_672 is highly correlated with num_orders and 6 other fieldsHigh correlation
rolling_mean_336h is highly correlated with num_orders and 8 other fieldsHigh correlation
rolling_mean_168h is highly correlated with num_orders and 8 other fieldsHigh correlation
trend_lag168 is highly correlated with num_orders and 4 other fieldsHigh correlation
seasonal_lag168 is highly correlated with num_orders and 6 other fieldsHigh correlation
num_orders is highly correlated with month and 8 other fieldsHigh correlation
month is highly correlated with num_orders and 3 other fieldsHigh correlation
lag_168 is highly correlated with num_orders and 8 other fieldsHigh correlation
lag_336 is highly correlated with num_orders and 6 other fieldsHigh correlation
lag_504 is highly correlated with num_orders and 6 other fieldsHigh correlation
lag_672 is highly correlated with num_orders and 6 other fieldsHigh correlation
rolling_mean_336h is highly correlated with num_orders and 8 other fieldsHigh correlation
rolling_mean_168h is highly correlated with num_orders and 8 other fieldsHigh correlation
trend_lag168 is highly correlated with num_orders and 4 other fieldsHigh correlation
seasonal_lag168 is highly correlated with num_orders and 6 other fieldsHigh correlation
resid_lag168 is highly correlated with lag_168High correlation
num_orders is highly correlated with lag_168 and 5 other fieldsHigh correlation
month is highly correlated with trend_lag168High correlation
lag_168 is highly correlated with num_orders and 5 other fieldsHigh correlation
lag_336 is highly correlated with num_orders and 5 other fieldsHigh correlation
lag_504 is highly correlated with num_orders and 5 other fieldsHigh correlation
lag_672 is highly correlated with num_orders and 5 other fieldsHigh correlation
rolling_mean_336h is highly correlated with num_orders and 5 other fieldsHigh correlation
rolling_mean_168h is highly correlated with num_orders and 5 other fieldsHigh correlation
trend_lag168 is highly correlated with monthHigh correlation
num_orders is highly correlated with hour and 9 other fieldsHigh correlation
month is highly correlated with trend_lag168High correlation
hour is highly correlated with num_orders and 4 other fieldsHigh correlation
lag_168 is highly correlated with num_orders and 7 other fieldsHigh correlation
lag_336 is highly correlated with num_orders and 7 other fieldsHigh correlation
lag_504 is highly correlated with num_orders and 8 other fieldsHigh correlation
lag_672 is highly correlated with num_orders and 7 other fieldsHigh correlation
rolling_mean_336h is highly correlated with num_orders and 8 other fieldsHigh correlation
rolling_mean_168h is highly correlated with num_orders and 8 other fieldsHigh correlation
trend_lag168 is highly correlated with num_orders and 3 other fieldsHigh correlation
seasonal_lag168 is highly correlated with num_orders and 7 other fieldsHigh correlation
resid_lag168 is highly correlated with num_orders and 4 other fieldsHigh correlation
lag_168 has 168 (3.8%) missing values Missing
lag_336 has 336 (7.6%) missing values Missing
lag_504 has 504 (11.4%) missing values Missing
lag_672 has 672 (15.2%) missing values Missing
rolling_mean_336h has 337 (7.6%) missing values Missing
rolling_mean_168h has 169 (3.8%) missing values Missing
trend_lag168 has 180 (4.1%) missing values Missing
seasonal_lag168 has 168 (3.8%) missing values Missing
resid_lag168 has 180 (4.1%) missing values Missing
datetime has unique values Unique
dayofweek has 624 (14.1%) zeros Zeros
hour has 184 (4.2%) zeros Zeros

Reproduction

Analysis started2022-03-22 11:04:53.727129
Analysis finished2022-03-22 11:05:06.946933
Duration13.22 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

datetime
Date

UNIQUE

Distinct4416
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Minimum2018-03-01 00:00:00
Maximum2018-08-31 23:00:00
2022-03-22T16:05:07.005854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:07.085599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

num_orders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct251
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.4227808
Minimum0
Maximum462
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:07.187581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q154
median78
Q3107
95-th percentile166
Maximum462
Range462
Interquartile range (IQR)53

Descriptive statistics

Standard deviation45.02385342
Coefficient of variation (CV)0.5333140296
Kurtosis3.76808057
Mean84.4227808
Median Absolute Deviation (MAD)26
Skewness1.188955573
Sum372811
Variance2027.147377
MonotonicityNot monotonic
2022-03-22T16:05:07.262099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7365
 
1.5%
5758
 
1.3%
6658
 
1.3%
7854
 
1.2%
8452
 
1.2%
7751
 
1.2%
8350
 
1.1%
8049
 
1.1%
6948
 
1.1%
6148
 
1.1%
Other values (241)3883
87.9%
ValueCountFrequency (%)
01
 
< 0.1%
13
 
0.1%
26
 
0.1%
35
 
0.1%
45
 
0.1%
510
0.2%
613
0.3%
715
0.3%
85
 
0.1%
98
0.2%
ValueCountFrequency (%)
4621
 
< 0.1%
4371
 
< 0.1%
4081
 
< 0.1%
3421
 
< 0.1%
2951
 
< 0.1%
2812
< 0.1%
2761
 
< 0.1%
2733
0.1%
2721
 
< 0.1%
2681
 
< 0.1%

month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.505434783
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:07.320110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median5.5
Q37
95-th percentile8
Maximum8
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.713306101
Coefficient of variation (CV)0.3112026876
Kurtosis-1.274560483
Mean5.505434783
Median Absolute Deviation (MAD)1.5
Skewness-0.006006214519
Sum24312
Variance2.935417795
MonotonicityIncreasing
2022-03-22T16:05:07.370386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3744
16.8%
5744
16.8%
7744
16.8%
8744
16.8%
4720
16.3%
6720
16.3%
ValueCountFrequency (%)
3744
16.8%
4720
16.3%
5744
16.8%
6720
16.3%
7744
16.8%
8744
16.8%
ValueCountFrequency (%)
8744
16.8%
7744
16.8%
6720
16.3%
5744
16.8%
4720
16.3%
3744
16.8%

dayofweek
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.005434783
Minimum0
Maximum6
Zeros624
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:07.418166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.990684396
Coefficient of variation (CV)0.6623615349
Kurtosis-1.235249788
Mean3.005434783
Median Absolute Deviation (MAD)2
Skewness-0.007504656223
Sum13272
Variance3.962824364
MonotonicityNot monotonic
2022-03-22T16:05:07.462619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3648
14.7%
4648
14.7%
5624
14.1%
6624
14.1%
0624
14.1%
1624
14.1%
2624
14.1%
ValueCountFrequency (%)
0624
14.1%
1624
14.1%
2624
14.1%
3648
14.7%
4648
14.7%
5624
14.1%
6624
14.1%
ValueCountFrequency (%)
6624
14.1%
5624
14.1%
4648
14.7%
3648
14.7%
2624
14.1%
1624
14.1%
0624
14.1%

hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros184
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:07.515235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.922970448
Coefficient of variation (CV)0.6019974302
Kurtosis-1.20417852
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum50784
Variance47.92751982
MonotonicityNot monotonic
2022-03-22T16:05:07.570089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0184
 
4.2%
1184
 
4.2%
22184
 
4.2%
21184
 
4.2%
20184
 
4.2%
19184
 
4.2%
18184
 
4.2%
17184
 
4.2%
16184
 
4.2%
15184
 
4.2%
Other values (14)2576
58.3%
ValueCountFrequency (%)
0184
4.2%
1184
4.2%
2184
4.2%
3184
4.2%
4184
4.2%
5184
4.2%
6184
4.2%
7184
4.2%
8184
4.2%
9184
4.2%
ValueCountFrequency (%)
23184
4.2%
22184
4.2%
21184
4.2%
20184
4.2%
19184
4.2%
18184
4.2%
17184
4.2%
16184
4.2%
15184
4.2%
14184
4.2%

lag_168
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct234
Distinct (%)5.5%
Missing168
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean81.6584275
Minimum0
Maximum462
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:07.766772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q153
median77
Q3104
95-th percentile157
Maximum462
Range462
Interquartile range (IQR)51

Descriptive statistics

Standard deviation41.84639908
Coefficient of variation (CV)0.5124565873
Kurtosis3.717051696
Mean81.6584275
Median Absolute Deviation (MAD)26
Skewness1.059515717
Sum346885
Variance1751.121116
MonotonicityNot monotonic
2022-03-22T16:05:07.833971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7365
 
1.5%
5758
 
1.3%
6658
 
1.3%
7853
 
1.2%
8452
 
1.2%
7751
 
1.2%
8350
 
1.1%
8049
 
1.1%
6948
 
1.1%
6148
 
1.1%
Other values (224)3716
84.1%
(Missing)168
 
3.8%
ValueCountFrequency (%)
01
 
< 0.1%
13
 
0.1%
26
 
0.1%
35
 
0.1%
45
 
0.1%
510
0.2%
613
0.3%
715
0.3%
85
 
0.1%
98
0.2%
ValueCountFrequency (%)
4621
< 0.1%
4371
< 0.1%
2811
< 0.1%
2732
< 0.1%
2721
< 0.1%
2541
< 0.1%
2531
< 0.1%
2511
< 0.1%
2491
< 0.1%
2481
< 0.1%

lag_336
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct223
Distinct (%)5.5%
Missing336
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean79.53848039
Minimum0
Maximum437
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:07.908655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q152.75
median76
Q3102
95-th percentile152
Maximum437
Range437
Interquartile range (IQR)49.25

Descriptive statistics

Standard deviation39.61629508
Coefficient of variation (CV)0.4980770928
Kurtosis2.408662678
Mean79.53848039
Median Absolute Deviation (MAD)25
Skewness0.8510084143
Sum324517
Variance1569.450836
MonotonicityNot monotonic
2022-03-22T16:05:07.982478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7363
 
1.4%
6658
 
1.3%
5758
 
1.3%
7853
 
1.2%
8452
 
1.2%
8350
 
1.1%
7750
 
1.1%
6148
 
1.1%
8048
 
1.1%
6247
 
1.1%
Other values (213)3553
80.5%
(Missing)336
 
7.6%
ValueCountFrequency (%)
01
 
< 0.1%
13
 
0.1%
26
 
0.1%
35
 
0.1%
45
 
0.1%
510
0.2%
613
0.3%
715
0.3%
85
 
0.1%
98
0.2%
ValueCountFrequency (%)
4371
< 0.1%
2731
< 0.1%
2531
< 0.1%
2511
< 0.1%
2491
< 0.1%
2481
< 0.1%
2451
< 0.1%
2341
< 0.1%
2311
< 0.1%
2301
< 0.1%

lag_504
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct213
Distinct (%)5.4%
Missing504
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean77.59458078
Minimum0
Maximum253
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:08.056554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q152
median74
Q3100
95-th percentile145.45
Maximum253
Range253
Interquartile range (IQR)48

Descriptive statistics

Standard deviation37.7079744
Coefficient of variation (CV)0.4859614425
Kurtosis0.8456628968
Mean77.59458078
Median Absolute Deviation (MAD)24
Skewness0.6377937443
Sum303550
Variance1421.891333
MonotonicityNot monotonic
2022-03-22T16:05:08.133822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7362
 
1.4%
5758
 
1.3%
6657
 
1.3%
7851
 
1.2%
7750
 
1.1%
8349
 
1.1%
8449
 
1.1%
6148
 
1.1%
8048
 
1.1%
7446
 
1.0%
Other values (203)3394
76.9%
(Missing)504
 
11.4%
ValueCountFrequency (%)
01
 
< 0.1%
13
 
0.1%
26
 
0.1%
35
 
0.1%
45
 
0.1%
510
0.2%
613
0.3%
715
0.3%
85
 
0.1%
98
0.2%
ValueCountFrequency (%)
2531
< 0.1%
2511
< 0.1%
2481
< 0.1%
2451
< 0.1%
2341
< 0.1%
2301
< 0.1%
2291
< 0.1%
2241
< 0.1%
2232
< 0.1%
2221
< 0.1%

lag_672
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct206
Distinct (%)5.5%
Missing672
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean76.22622863
Minimum0
Maximum253
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:08.206624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q151
median73
Q398
95-th percentile143
Maximum253
Range253
Interquartile range (IQR)47

Descriptive statistics

Standard deviation36.87755256
Coefficient of variation (CV)0.4837908581
Kurtosis0.7875990059
Mean76.22622863
Median Absolute Deviation (MAD)23
Skewness0.6105734582
Sum285391
Variance1359.953883
MonotonicityNot monotonic
2022-03-22T16:05:08.280114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7362
 
1.4%
5758
 
1.3%
6657
 
1.3%
7849
 
1.1%
7749
 
1.1%
8447
 
1.1%
6147
 
1.1%
8346
 
1.0%
4846
 
1.0%
7445
 
1.0%
Other values (196)3238
73.3%
(Missing)672
 
15.2%
ValueCountFrequency (%)
01
 
< 0.1%
13
 
0.1%
26
 
0.1%
35
 
0.1%
45
 
0.1%
510
0.2%
613
0.3%
715
0.3%
85
 
0.1%
98
0.2%
ValueCountFrequency (%)
2531
< 0.1%
2511
< 0.1%
2451
< 0.1%
2341
< 0.1%
2291
< 0.1%
2241
< 0.1%
2231
< 0.1%
2221
< 0.1%
2161
< 0.1%
2151
< 0.1%

rolling_mean_336h
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct370
Distinct (%)9.1%
Missing337
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean79.52378034
Minimum2.5
Maximum355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:08.351866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile24.5
Q156
median76.5
Q3100
95-th percentile141.5
Maximum355
Range352.5
Interquartile range (IQR)44

Descriptive statistics

Standard deviation35.12166444
Coefficient of variation (CV)0.4416498346
Kurtosis1.570175869
Mean79.52378034
Median Absolute Deviation (MAD)22
Skewness0.6474311148
Sum324377.5
Variance1233.531313
MonotonicityNot monotonic
2022-03-22T16:05:08.422924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.534
 
0.8%
7533
 
0.7%
7932
 
0.7%
60.531
 
0.7%
62.530
 
0.7%
65.530
 
0.7%
55.530
 
0.7%
72.530
 
0.7%
7730
 
0.7%
7830
 
0.7%
Other values (360)3769
85.3%
(Missing)337
 
7.6%
ValueCountFrequency (%)
2.53
0.1%
3.51
 
< 0.1%
4.51
 
< 0.1%
51
 
< 0.1%
5.53
0.1%
63
0.1%
72
 
< 0.1%
7.51
 
< 0.1%
82
 
< 0.1%
8.57
0.2%
ValueCountFrequency (%)
3551
< 0.1%
302.51
< 0.1%
237.51
< 0.1%
216.51
< 0.1%
215.51
< 0.1%
211.51
< 0.1%
2091
< 0.1%
205.51
< 0.1%
200.51
< 0.1%
2001
< 0.1%

rolling_mean_168h
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct390
Distinct (%)9.2%
Missing169
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean81.64268896
Minimum2.5
Maximum367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:08.497639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile25.15
Q156.5
median77.5
Q3102
95-th percentile147.85
Maximum367
Range364.5
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation37.3685781
Coefficient of variation (CV)0.4577088111
Kurtosis2.723833115
Mean81.64268896
Median Absolute Deviation (MAD)22.5
Skewness0.886100439
Sum346736.5
Variance1396.410629
MonotonicityNot monotonic
2022-03-22T16:05:08.568225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.534
 
0.8%
7533
 
0.7%
7932
 
0.7%
7831
 
0.7%
60.531
 
0.7%
54.530
 
0.7%
7730
 
0.7%
55.530
 
0.7%
62.530
 
0.7%
72.530
 
0.7%
Other values (380)3936
89.1%
(Missing)169
 
3.8%
ValueCountFrequency (%)
2.53
0.1%
3.51
 
< 0.1%
4.51
 
< 0.1%
51
 
< 0.1%
5.53
0.1%
63
0.1%
72
 
< 0.1%
7.51
 
< 0.1%
82
 
< 0.1%
8.57
0.2%
ValueCountFrequency (%)
3671
< 0.1%
3551
< 0.1%
3431
< 0.1%
302.51
< 0.1%
248.52
< 0.1%
237.51
< 0.1%
2361
< 0.1%
2351
< 0.1%
2241
< 0.1%
2181
< 0.1%

trend_lag168
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3450
Distinct (%)81.4%
Missing180
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean81.73669145
Minimum42.45833333
Maximum159.4166667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2022-03-22T16:05:08.641315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum42.45833333
5-th percentile51.09895833
Q163.16666667
median78.5625
Q396.83854167
95-th percentile127.1458333
Maximum159.4166667
Range116.9583333
Interquartile range (IQR)33.671875

Descriptive statistics

Standard deviation23.03692575
Coefficient of variation (CV)0.2818431397
Kurtosis-0.06197980381
Mean81.73669145
Median Absolute Deviation (MAD)16.71875
Skewness0.6482450374
Sum346236.625
Variance530.6999479
MonotonicityNot monotonic
2022-03-22T16:05:08.710921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.229166676
 
0.1%
82.06256
 
0.1%
64.55
 
0.1%
100.27083335
 
0.1%
69.708333335
 
0.1%
89.770833335
 
0.1%
63.06254
 
0.1%
68.291666674
 
0.1%
68.958333334
 
0.1%
68.458333334
 
0.1%
Other values (3440)4188
94.8%
(Missing)180
 
4.1%
ValueCountFrequency (%)
42.458333332
< 0.1%
42.752
< 0.1%
42.81251
< 0.1%
42.833333331
< 0.1%
43.145833331
< 0.1%
43.166666671
< 0.1%
43.458333331
< 0.1%
43.645833331
< 0.1%
43.854166671
< 0.1%
44.06251
< 0.1%
ValueCountFrequency (%)
159.41666671
< 0.1%
158.85416671
< 0.1%
158.39583331
< 0.1%
157.60416671
< 0.1%
156.58333331
< 0.1%
156.47916671
< 0.1%
156.1251
< 0.1%
156.04166671
< 0.1%
155.83333331
< 0.1%
155.77083331
< 0.1%

seasonal_lag168
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)0.6%
Missing168
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean8.563980243 × 10-16
Minimum-59.18267114
Maximum60.2481121
Zeros0
Zeros (%)0.0%
Negative1947
Negative (%)44.1%
Memory size34.6 KiB
2022-03-22T16:05:08.772208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-59.18267114
5-th percentile-55.10446076
Q1-11.74773262
median3.261488692
Q314.66896251
95-th percentile29.60410026
Maximum60.2481121
Range119.4307832
Interquartile range (IQR)26.41669513

Descriptive statistics

Standard deviation26.15670324
Coefficient of variation (CV)3.054269452 × 1016
Kurtosis0.5671612721
Mean8.563980243 × 10-16
Median Absolute Deviation (MAD)14.54912341
Skewness-0.3639205388
Sum2.728484105 × 10-12
Variance684.1731244
MonotonicityNot monotonic
2022-03-22T16:05:08.827320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
-2.133376973177
 
4.0%
-16.72160557177
 
4.0%
13.59681428177
 
4.0%
10.67024325177
 
4.0%
-2.696560033177
 
4.0%
-7.124269505177
 
4.0%
-15.52795803177
 
4.0%
7.920015559177
 
4.0%
29.60410026177
 
4.0%
3.731832498177
 
4.0%
Other values (14)2478
56.1%
ValueCountFrequency (%)
-59.18267114177
4.0%
-55.10446076177
4.0%
-41.56302178177
4.0%
-16.72160557177
4.0%
-15.52795803177
4.0%
-13.35241158177
4.0%
-11.21283963177
4.0%
-9.191664769177
4.0%
-7.124269505177
4.0%
-2.696560033177
4.0%
ValueCountFrequency (%)
60.2481121177
4.0%
29.60410026177
4.0%
28.98274325177
4.0%
25.00050281177
4.0%
20.17707385177
4.0%
17.88540718177
4.0%
13.59681428177
4.0%
10.67024325177
4.0%
8.759268746177
4.0%
7.920015559177
4.0%

resid_lag168
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4185
Distinct (%)98.8%
Missing180
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean-0.01476252105
Minimum-71.49947822
Maximum279.3714234
Zeros0
Zeros (%)0.0%
Negative2216
Negative (%)50.2%
Memory size34.6 KiB
2022-03-22T16:05:08.893996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-71.49947822
5-th percentile-35.23351169
Q1-14.54831702
median-1.376015103
Q312.79765293
95-th percentile37.64481823
Maximum279.3714234
Range350.8709016
Interquartile range (IQR)27.34596995

Descriptive statistics

Standard deviation23.62257685
Coefficient of variation (CV)-1600.172272
Kurtosis9.719783155
Mean-0.01476252105
Median Absolute Deviation (MAD)13.59437614
Skewness1.256619629
Sum-62.53403916
Variance558.026137
MonotonicityNot monotonic
2022-03-22T16:05:08.964101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11.16933253
 
0.1%
-8.3757304952
 
< 0.1%
0.25416476932
 
< 0.1%
23.552092822
 
< 0.1%
-5.7286448852
 
< 0.1%
-7.7144144662
 
< 0.1%
13.396355122
 
< 0.1%
3.1463551152
 
< 0.1%
9.9794607622
 
< 0.1%
-23.575980952
 
< 0.1%
Other values (4175)4215
95.4%
(Missing)180
 
4.1%
ValueCountFrequency (%)
-71.499478221
< 0.1%
-67.920243251
< 0.1%
-67.836909911
< 0.1%
-67.149409911
< 0.1%
-63.414778761
< 0.1%
-62.210088421
< 0.1%
-61.458266921
< 0.1%
-60.201208641
< 0.1%
-59.07427331
< 0.1%
-59.024409911
< 0.1%
ValueCountFrequency (%)
279.37142341
< 0.1%
269.51725681
< 0.1%
139.94906461
< 0.1%
123.06438791
< 0.1%
114.26042621
< 0.1%
113.72968841
< 0.1%
98.204756751
< 0.1%
97.906259491
< 0.1%
97.468759491
< 0.1%
91.042188451
< 0.1%

Interactions

2022-03-22T16:05:05.744788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:55.954092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.783633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.534412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.368766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.118119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.945244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.697431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.527871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.459969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.269029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.068130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.955595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.801443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.015870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.839521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.652133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.424564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.175165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.001109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.763694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.589170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.521695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.327978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.128101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.015885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.862263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.151023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.896195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.710669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.483077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.234010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.061560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.826969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.650989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.585673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.387744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.188396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.077940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.922556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.209212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.955419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.770265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.541238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.294233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.119638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.892034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.826566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.652841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.450073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.248286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.139877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.979968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.264307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.010250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.826998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.594130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.348476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.174059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.952572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.888234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.715112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.509054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.304649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.196732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:06.036851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.318895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.065451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.886163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.648364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.476896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.228298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.012543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.949210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.775598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.567703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.361323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.253861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:06.093928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.373225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.120692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.943767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.706111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.531496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.282645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.073217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.010234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.834744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.625749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.418406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.311270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:06.153188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.429399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.177820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.002914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.762087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.587405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.339986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.135976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.071852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.895331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.684958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.582889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.370410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:06.211917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.485685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.233761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.061860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.820049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.643768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.398927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.198968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.134288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.954613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.745527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.641038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.429903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:06.275890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.546758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.295802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.124664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.881460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.703872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.458481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.266528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.200618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.018327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.808854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.706671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.495266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:06.339339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.608238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.356564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.187358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.942534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.763697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.518101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.333394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.267594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.081577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.873503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.769842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.561540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:06.400182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.667201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.415913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.247432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.002066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.823107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.576314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.398348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.332981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.143207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.936940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.831769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.622896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:06.460834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:56.725520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:57.474739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:58.308082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.059885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:04:59.885927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:00.634421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:01.463680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:02.396371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:03.205993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.003844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:04.894066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-22T16:05:05.684323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-22T16:05:09.030399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-22T16:05:09.123978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-22T16:05:09.217631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-22T16:05:09.311922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-22T16:05:06.560439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-22T16:05:06.695069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-22T16:05:06.796105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-22T16:05:06.885570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

datetimenum_ordersmonthdayofweekhourlag_168lag_336lag_504lag_672rolling_mean_336hrolling_mean_168htrend_lag168seasonal_lag168resid_lag168
02018-03-01 00:00:00124330NaNNaNNaNNaNNaNNaNNaNNaNNaN
12018-03-01 01:00:0085331NaNNaNNaNNaNNaNNaNNaNNaNNaN
22018-03-01 02:00:0071332NaNNaNNaNNaNNaNNaNNaNNaNNaN
32018-03-01 03:00:0066333NaNNaNNaNNaNNaNNaNNaNNaNNaN
42018-03-01 04:00:0043334NaNNaNNaNNaNNaNNaNNaNNaNNaN
52018-03-01 05:00:006335NaNNaNNaNNaNNaNNaNNaNNaNNaN
62018-03-01 06:00:0012336NaNNaNNaNNaNNaNNaNNaNNaNNaN
72018-03-01 07:00:0015337NaNNaNNaNNaNNaNNaNNaNNaNNaN
82018-03-01 08:00:0034338NaNNaNNaNNaNNaNNaNNaNNaNNaN
92018-03-01 09:00:0069339NaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

datetimenum_ordersmonthdayofweekhourlag_168lag_336lag_504lag_672rolling_mean_336hrolling_mean_168htrend_lag168seasonal_lag168resid_lag168
44062018-08-31 14:00:00133841488.0126.092.075.0122.0116.0143.875000-9.191665-46.683335
44072018-08-31 15:00:001168415117.0104.0106.096.0115.0102.5145.7500003.731832-32.481832
44082018-08-31 16:00:001978416188.0204.0174.0140.0154.0152.5149.64583329.6041008.750066
44092018-08-31 17:00:002178417170.0165.0138.0142.0184.5179.0153.8541677.9200168.225818
44102018-08-31 18:00:002078418137.0139.0131.091.0152.0153.5156.041667-15.527958-3.513709
44112018-08-31 19:00:001368419113.084.098.091.0111.5125.0155.833333-7.124270-35.709064
44122018-08-31 20:00:001548420179.0126.0114.087.0105.0146.0155.770833-2.69656025.925727
44132018-08-31 21:00:001598421166.0144.0143.0123.0135.0172.5156.58333310.670243-1.253577
44142018-08-31 22:00:002238422242.0167.0188.0170.0155.5204.0155.47916713.59681472.924019
44152018-08-31 23:00:002058423173.0155.0162.0123.0161.0207.5152.83333325.000503-4.833836